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Single-Cell Factor Localization on Chromatin using Ultra-Low Input Cleavage Under Targets and Release using Nuclease

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Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence

Marshall Case¹, Matthew Smith^1,2, Jordan Vinh³

¹Chemical Engineering, University of Michigan, Ann Arbor, MI 48109.

Proceedings of the National Academy of Sciences of the United States of America

|March 7, 2024

Summary

This summary is machine-generated.

This study introduces a machine learning framework to predict protein properties from directed evolution experiments. The approach uses linear models to efficiently identify optimized protein variants with improved functions.

Keywords:

directed evolution machine learning protein engineering

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Area of Science:

Biochemistry and Molecular Biology
Protein Engineering
Computational Biology

Background:

Proteins are essential biomolecules with diverse cellular functions.
Protein engineering aims to rapidly evolve proteins for improved properties.
High-throughput methods enhance directed evolution, but data interpretation remains challenging.

Purpose of the Study:

To develop a framework for predicting continuous protein properties from directed evolution data.
To improve protein optimization by leveraging interpretable, linear machine learning models.
To identify lead protein candidates with enhanced functions.

Main Methods:

Developed a framework using interpretable, linear machine learning models.
Utilized data from simple, imprecise experimental estimates of protein fitness.
Applied the framework to predict affinity and specificity from cell sorting data for stapled peptides.
Coupled integer linear programming with machine learning mutation scores for optimization.

Main Results:

Linear machine learning models accurately predict continuous protein properties from directed evolution data.
The framework's predictive capabilities approach those of more precise but expensive methods.
Protein fitness space is reasonably modeled by linear relationships among sequence mutations.
Successfully identified protein variants with improved and co-optimal properties in prospective tests.

Conclusions:

The developed framework offers a versatile tool for analyzing and identifying improved protein variants.
Predicting continuous protein properties from readily available deep sequencing data is feasible.
Linear relationships effectively model protein fitness landscapes, enabling efficient optimization.